A proposal in which they use the test set to adapt the model is the work of [1]}. In particular, they fine-tune a pre-built NMT model for each sentence in the test set. They use three methods to retrieve the sentences that are the most similar to a sentence of the test set: (i) Levenshtein distance [2]}; (ii) cosine similarity of the average of the word embeddings [3]}; and (iii) the cosine similarity between hidden states of the encoder in NMT. The main difference with our work is that they adapt the model sentence-wise (one model for each sentence) whereas the adaptations presented here are document-wise (one model for each test set). Although performing adaptations sentence-wise gives more fine-grained adaptations, it also has several disadvantages: (i) the computational cost is higher as there are several iterations (as many as sentences in the test set) of selecting data and fine-tuning; (ii) the usage of the data is less efficient as a same sentence can be extracted multiple times (in different iterations); and (iii) using different models for each sentence has the potential risk of performing translations that are not consistent throughout the entire document.
